Following several months of feedback in response to its 2019 discussion paper on the need for regulatory clarity across the emerging AI landscape, the FDA has released an AI and Machine Learning action plan focused on advancing the agency’s management of advanced medical software.

According to Bakul Patel, director of the Digital Health Center of Excellence in the Center for Devices and Radiological Health (CDRH), the Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan “outlines a holistic approach based on total product lifecycle oversight to further the enormous potential that these technologies have to improve patient care while delivering safe and effective software functionality that improves the quality of care that patients receive. To stay current and address patient safety and improve access to these promising technologies, we anticipate that this action plan will continue to evolve over time.”

In the new action plan, the FDA signals its intent to further develop the proposed regulatory framework for AI/ML-based SaMD, including issuing draft guidance on a predetermined change control plan, which pertains to software that learns over time.

“One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance,” the plan explains. “FDA’s vision is that, with appropriately tailored total product lifecycle-based regulatory oversight, AI/ML-based Software as a Medical Device (SaMD) will deliver safe and effective software functionality that improves the quality of care that patients receive.

The agency plans to publish this draft guidance in 2021, with other areas of development to include refinement of the identification of types of modifications appropriate under the framework, as well as specifics on the focused review, such as the process for submission/review and the content of a submission.

The agency will also aim to support the development of good machine learning practices to evaluate and improve machine learning algorithms. The FDA noted that the development and adoption of AI/ML best practices is important not only to guide product design, but also to facilitate the oversight of these advanced devices.

The proposed framework “would enable FDA to provide a reasonable assurance of safety and effectiveness while embracing the iterative improvement power of artificial intelligence and machine learning-based software as a medical device,” the plan said.

Moreover, addressing widespread stakeholder concerns about algorithmic bias, the FDA says it will support regulatory science to develop methodology to evaluate and improve machine learning algorithms.

FDA "recognizes the crucial importance for medical devices to be well suited for a racially and ethnically diverse intended patient population and the need for improved methodologies for the identification and improvement of machine learning algorithms," the action plan said.